基于秩次R类稳健回归

目的从参数估计、稳健性质、回归诊断应用等方面介绍基于广义秩次的一类稳健回归分析方法—R和GR估计。方法在SAS的IML模块下模拟其对非正态误差分布表现、正态误差下的估计效率并进行实例分析。结果误差为cauchy分布时,R估计优于LS估计,X空间存在离群值时,GR估计优于R和LS估计,误差为正态分布时,R与CR估计效率达95%。结论R和GR估计为是一种估计效率较高的稳健回归方法,其中GR估计可同时避免X和Y空间离群点。

Rank-based Robust Regression Method

To explore the rank-based robust regression,R and GR estimator with their estimation methods,robustness properties and regression diagnostics.Methods Using SAS/IML,Simulation study were carried on under normal and nonormal distribution,and an medical research application were given.Results When errors Cauchy distributed,R estimator performs better than LS,when outliers occur in X space,GR estimator performs better than R and LS,when errors normal distributed,estimation efficiency of GR and R achieve 95% or so.Conclusion R and GR estimators are efficient and robust,and GR estimator can resists outliers form both X and Y space.